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/*! \file
    \brief Reference implementation for complex-valued GEMM in host-side code.
*/

#pragma once

#include "cutlass/coord.h"
#include "cutlass/complex.h"
#include "cutlass/numeric_types.h"
#include "cutlass/functional.h"
#include "cutlass/numeric_conversion.h"

#include "cutlass/tensor_view.h"
#include "cutlass/gemm/gemm.h"

namespace cutlass {
namespace reference {
namespace host {

////////////////////////////////////////////////////////////////////////////////////////////////////

/// Computes a general matrix product among matrices (tensors of rank=2) pointed
/// to by TensorRef objects.
///
/// Explicitly naming types needed by this template can be cumbersome,
/// particularly for the accumulator type, so a function argument
/// 'initial_accum' is exposed. Passing AccumulatorType(0) as the last function
/// argument can be easier than naming all template arguments explicitly.
template <typename ElementA, typename LayoutA, typename ElementB,
          typename LayoutB, typename ElementC, typename LayoutC,
          typename ScalarType, typename ComputeType,
          typename ConvertOp = NumericConverter<ElementC, ScalarType>,
          typename InnerProductOp = multiply_add<ComputeType> >
void GemmComplex(gemm::GemmCoord problem_size, ScalarType alpha,
                 TensorRef<ElementA, LayoutA> tensor_a,
                 ComplexTransform transform_a,
                 TensorRef<ElementB, LayoutB> tensor_b,
                 ComplexTransform transform_b, ScalarType beta,
                 TensorRef<ElementC, LayoutC> tensor_c,
                 TensorRef<ElementC, LayoutC> tensor_d,
                 ComputeType initial_accum, int batch_count = 1,
                 int64_t batch_stride_A = 0, int64_t batch_stride_B = 0,
                 int64_t batch_stride_C = 0, int64_t batch_stride_D = 0) {
    static_assert(
            LayoutA::kRank == 2 && LayoutB::kRank == 2 && LayoutC::kRank == 2,
            "Tensors must be of rank 2");

    // Note: batch is ignored.
    int const M = problem_size.m();
    int const N = problem_size.n();
    int const K = problem_size.k();

    // Blocking necessary to speedup reference implementation
    int const Mblock = 16;
    int const Nblock = 16;

    ConvertOp convert_op;
    InnerProductOp inner_product_op;

    for (int batch_idx = 0; batch_idx < batch_count; ++batch_idx) {
        // Compute matrix product using blocks
        for (int row_block = 0; row_block < M; row_block += Mblock) {
            for (int col_block = 0; col_block < N; col_block += Nblock) {
                ComputeType accum[Mblock][Nblock];

                for (int j = 0; j < Nblock; j++) {
                    for (int i = 0; i < Mblock; i++) {
                        accum[i][j] = initial_accum;
                    }
                }

                for (int k_block = 0; k_block < K; ++k_block) {
                    for (int j = 0; j < Nblock; j++) {
                        for (int i = 0; i < Mblock; i++) {
                            int row = row_block + i;
                            int col = col_block + j;

                            if (row < M && col < N) {
                                ElementA a =
                                        tensor_a.at(MatrixCoord(row, k_block));
                                ElementB b =
                                        tensor_b.at(MatrixCoord(k_block, col));

                                ComputeType a_ik = ComputeType(a);
                                ComputeType b_kj = ComputeType(b);

                                if (transform_a ==
                                    ComplexTransform::kConjugate) {
                                    a_ik = conj(a_ik);
                                }

                                if (transform_b ==
                                    ComplexTransform::kConjugate) {
                                    b_kj = conj(b_kj);
                                }

                                accum[i][j] = inner_product_op(a_ik, b_kj,
                                                               accum[i][j]);
                            }
                        }
                    }
                }

                for (int j = 0; j < Nblock; j++) {
                    for (int i = 0; i < Mblock; i++) {
                        int row = row_block + i;
                        int col = col_block + j;

                        MatrixCoord coord = MatrixCoord(row, col);

                        if (row < M && col < N) {
                            tensor_d.at(coord) = convert_op(
                                    alpha * ScalarType(accum[i][j]) +
                                    beta * ScalarType(tensor_c.at(coord)));
                        }
                    }
                }

            }  // for (col_block)
        }      // for (row_block)

        tensor_a.add_pointer_offset(batch_stride_A);
        tensor_b.add_pointer_offset(batch_stride_B);
        tensor_c.add_pointer_offset(batch_stride_C);
        tensor_d.add_pointer_offset(batch_stride_D);

    }  // for (batch_idx)
}

////////////////////////////////////////////////////////////////////////////////////////////////////

/// Computes a general matrix product among matrices (tensors of rank=2) pointed
/// to by TensorRef objects.
///
/// This assumes the accumulator type is the same type as the scalars.
template <typename ElementA, typename LayoutA, typename ElementB,
          typename LayoutB, typename ElementC, typename LayoutC,
          typename ScalarType>
void GemmComplex(gemm::GemmCoord problem_size, ScalarType alpha,
                 TensorRef<ElementA, LayoutA> tensor_a,
                 ComplexTransform transform_a,
                 TensorRef<ElementB, LayoutB> tensor_b,
                 ComplexTransform transform_b, ScalarType beta,
                 TensorRef<ElementC, LayoutC> tensor_c,
                 TensorRef<ElementC, LayoutC> tensor_d) {
    GemmComplex(problem_size, alpha, tensor_a, transform_a, tensor_b,
                transform_b, beta, tensor_c, tensor_d, ScalarType(0));
}

////////////////////////////////////////////////////////////////////////////////////////////////////

}  // namespace host
}  // namespace reference
}  // namespace cutlass
